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code demonstrates how to use a few-shot approach with GPT-Neo, an open-source language model by EleutherAI, to generate text for complex topics like blockchain technology in supply chain management. The few-shot learning method is effective because it enables the model to produce coherent responses on specialized topics with minimal examples, bridging the gap between zero-shot (no examples) and full training.\n","\n","In this example, we use the model to produce a \"zero-shot\" response as a preliminary step, which can be helpful for evaluating how well the model understands the prompt without examples. By analyzing the zero-shot results, we gain insight into the model’s baseline capabilities before moving to a few-shot approach, which would include providing some examples in the prompt if desired."],"metadata":{"id":"qpUMr2nKPal7"}},{"cell_type":"markdown","source":["### First of all let's install required packages"],"metadata":{"id":"yCkEi4peRYKi"}},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L3G9LoX6PY4t","executionInfo":{"status":"ok","timestamp":1730490586471,"user_tz":0,"elapsed":14857,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}},"outputId":"d08abf54-127d-4d4a-92c1-4c738ed6b9c0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.24.7)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n","Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n","Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.6)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.10.0)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4.0)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n","Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.0+cu121)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.16.1)\n","Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.4.2)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.10.0)\n","Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (3.0.2)\n"]}],"source":["! pip install transformers\n","! pip install torch"]},{"cell_type":"markdown","source":["### Import Required Libraries\n","We import the pipeline and set_seed functions from Hugging Face's Transformers library. The pipeline function simplifies access to various pre-trained models, while set_seed ensures reproducibility in generated text, so the results remain consistent across runs."],"metadata":{"id":"E9teYwiaRns5"}},{"cell_type":"code","source":["# Import necessary libraries\n","from transformers import pipeline, set_seed"],"metadata":{"id":"Z5hMOec2RnFf","executionInfo":{"status":"ok","timestamp":1730490707870,"user_tz":0,"elapsed":29267,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}}},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":["Here, we set up the text generation pipeline with GPT-Neo 1.3B, a popular model known for generating high-quality text in a free and open-source format. By specifying \"text-generation\" in the pipeline, we streamline the process of generating text based on prompts, making the code straightforward and efficient for various text-based tasks."],"metadata":{"id":"fwbne17YSEoB"}},{"cell_type":"code","source":["# Initialize Hugging Face pipeline with the GPT-Neo model\n","generator = pipeline(\"text-generation\", model=\"EleutherAI/gpt-neo-1.3B\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":372,"referenced_widgets":["7c81ec03420d4e93b50d892c1c458193","285248b4495d476680b720e536327f8e","6547f0f4d3744de88975a1d6f7c36b46","1b6e8caaf45b4f8eb54669e6301ed174","0cc1214967a947ae91ab384d713cee0c","1d82cff7f03c414e90108703114d75db","a48f3188c01a48a2905aab0a3908fc23","a65c9aaf421f4fb08885e239c770bfac","d70dacc80815438e874fd5a5441885db","f6874850e53649bbb87fcebe2c7d30d6","64b33a0ce50d47c9b2abf82cbfc51c8a","d05b155d596b411fac2a9879b73a870b","94b62b0c5f9043e9be7d2ec60861f3d0","3c5efeb2bdd742f9a80480380ca74cde","757adde223724ce09f065eb8b8b9f3d2","cbf5ce736561497f94401b76f83c86d8","a1c2728218b742359ecdc4fbb51d0728","3babe4db83e1485c8049abc9a34c1579","20aff74eb9b34452a63bf736daf37003","64b44b40b8e44b62b0c9f0f1ef9ed059","ed0130c32d21408ca6191238b5c85e94","4cf03a69ce8545b9a3b852b44f329b00","10afa5a5f85c44ab8c846b520b453a60","b7f94de61dbe4966a65c2bcfa66e8d8f","1989b78133524e1aaddaaaed3d693afd","c1fbe935684742969bcb7c245be936b3","a3906cd29c5746ccb68639c43d4c4515","2bc699dda7fe42579243dac2d9a8093b","f4a47a2bc08d449682485634569e66a2","2247d72b9b324477a99526a46240da22","81cd5af7b8b54c5fb57d49d96ace0dbc","e6833bf171064e4b9e9648fd57b8852e","f20aac480650479888f30891303ae2f9","22d2377e6de942c6ad574ba90c6390bd","cb97844e76d14ca5a6bdfd4fed80edcc","8783735df47d479abf9bc6fe0bc96aa6","a3d215dcd5374d1cad6f6e30aa44b1e3","ea20aeabbfbc4fc99fb933f8ee0f74b5","638f7a8a1e4b4099bf311140c8573d25","1369f1a409734ba69792fbcb2323befc","1ba7a8bf55154e8eb40638abc954dcd2","b59ee05537774d04912ba10f351aae8b","cacf9e76ff834075a10fe98cd029e230","256337341ced455bb34550e392a2f646","4dc9e03db3b84bbe9c91448f04072a43","cb650aa099aa4c269e6db2c6da8dd0ee","9f4b60cb481446e29f9d5ec5388a66bd","03bf4ff95add44ffa19b09ad17d38c5a","e9090a3540e94719bd43156eee8e963d","dcae9e97c77347f8b56da291db8e9020","4da5f931f0944621b47f56721f2af062","3dfb77c150194360846b52efcfc13e90","386a3144b73742ed9131b79b74557a33","dc8683390ab54f2d882df457a634b531","95410fbfb7e54275ba5fded2629bf032","6161f3dc913d493782e6a1327ca13a49","e6f14b569d6b44a38df343d9cf6b51b6","fc5c1dd573ff424696b4c476e9a3366c","d99439e45f6b4e19b798530a61d3c6fc","aeb6faee2a084497a2ed1e0f4abc1193","00f3b1eb95054476a2b6761f439636ad","bcefd28ff89841a4ac5cf5855a3e063d","aa3036b4c9374444919aff6cff076e17","88212e554b5a4f9997dbec8289e27631","198489941f4d409b887dbeb62c81ec03","95491637e7214dc0aa5444b955ee47f9"]},"id":"BGPSjic_SDvn","executionInfo":{"status":"ok","timestamp":1730490764502,"user_tz":0,"elapsed":56637,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}},"outputId":"b7382a25-6166-4cdd-c8c0-39f55e3769bd"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/1.35k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7c81ec03420d4e93b50d892c1c458193"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/5.31G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d05b155d596b411fac2a9879b73a870b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/200 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"10afa5a5f85c44ab8c846b520b453a60"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"22d2377e6de942c6ad574ba90c6390bd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4dc9e03db3b84bbe9c91448f04072a43"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/90.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6161f3dc913d493782e6a1327ca13a49"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n","  warnings.warn(\n"]}]},{"cell_type":"markdown","source":["Setting a seed helps ensure that the model produces the same output each time the code is run, which is crucial for consistency, especially when testing or debugging code. The seed value 42 is arbitrary but widely used in programming as a default."],"metadata":{"id":"cXOJ4QmDSK0O"}},{"cell_type":"code","source":["# Set a seed for reproducibility\n","set_seed(42)"],"metadata":{"id":"XoxEiOOoSKE0","executionInfo":{"status":"ok","timestamp":1730490764503,"user_tz":0,"elapsed":2,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":["This variable stores a zero-shot prompt where the model is asked to explain blockchain technology in the context of supply chain management. A zero-shot prompt means no examples are given—the model must respond based solely on its pre-existing knowledge. This prompt tests the model’s initial understanding and is a starting point for comparing with few-shot results later."],"metadata":{"id":"md7tyZZLSQ8F"}},{"cell_type":"code","source":["# Define a zero-shot prompt asking the model to define a term\n","zero_shot_prompt = \"Explain the concept of blockchain technology in supply chain management.\"\n"],"metadata":{"id":"3c6WJXDHSQcB","executionInfo":{"status":"ok","timestamp":1730490764503,"user_tz":0,"elapsed":2,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}}},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":["Here, we call the generator to produce text based on the prompt. We specify max_length=100 to limit the response to 100 tokens and num_return_sequences=1 to get a single output sequence. This step runs the model and generates a response, allowing us to evaluate its ability to respond in a zero-shot setting.\n"],"metadata":{"id":"nFo9eY3TSZeZ"}},{"cell_type":"code","source":["# Generate the output\n","zero_shot_output = generator(zero_shot_prompt, max_length=100, num_return_sequences=1)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b2TmpCRqSVQO","executionInfo":{"status":"ok","timestamp":1730490882208,"user_tz":0,"elapsed":105343,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}},"outputId":"0df28484-90c5-4e83-97f1-ea5eec549c75"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stderr","text":["Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]}]},{"cell_type":"markdown","source":["This final snippet displays the model’s generated text. By printing the zero-shot output, we gain a baseline understanding of how the model interprets the concept without any additional examples, which provides insights into its default knowledge on the topic. From here, a few-shot prompt with examples can be used if further guidance or refinement is necessary."],"metadata":{"id":"v1uYIOr4Se2M"}},{"cell_type":"code","source":["# Print the output\n","print(\"Zero-shot Output:\")\n","print(zero_shot_output[0]['generated_text'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bia_P3EqSbul","executionInfo":{"status":"ok","timestamp":1730490882208,"user_tz":0,"elapsed":4,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}},"outputId":"c970cee1-3a19-4d21-f879-06622e733b5e"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Zero-shot Output:\n","Explain the concept of blockchain technology in supply chain management. Learn what blockchain technology is and how it allows a supply chain to go “untraceable.” Explore what some of the most well-known blockchain companies are doing with blockchain technology.\n","It focuses on the real-world applications of the technology, specifically in supply chain management. The course will be delivered in a format suitable for use alongside the CITP certification exam, and is designed to help prepare you for the CIT\n"]}]},{"cell_type":"markdown","source":["### Now let's talk about the Few-shot:\n","In few-shot learning, we provide the model with a couple of examples within the prompt to guide its response. This approach helps the model understand the specific structure or tone we want, improving its output quality, especially in tasks requiring nuanced or context-specific responses. In this example, we’re asking the model to explain \"blockchain in supply chain management\" by first showing it two example responses.\n","\n","\n","Purpose: The few_shot_prompt variable is designed to demonstrate how blockchain applies to supply chain management. By providing two examples, we show the model the kind of structured, informative response we expect it to generate.\n","Prompt Structure: We include a general question, followed by two specific examples that set the tone and content style. These examples cover key benefits like tracking, security, transparency, and auditing. Finally, “Your turn:” serves as a prompt to signal the model to continue in a similar manner."],"metadata":{"id":"sOi-7lkxS5W7"}},{"cell_type":"code","source":["# Define a few-shot prompt with examples included\n","few_shot_prompt = \"\"\"\n","Explain the concept of blockchain in supply chain management.\n","\n","\n","Example 1:\n","\"Blockchain technology in supply chain management enables secure tracking of goods across each stage. It helps reduce fraud and improves transparency.\"\n","\n","\n","Example 2:\n","\"By using blockchain, companies can ensure that products are sourced ethically and securely. Every transaction is recorded, making auditing straightforward.\"\n","\n","\n","Your turn:\n","\"\"\""],"metadata":{"id":"o1u9lVO7ShMQ","executionInfo":{"status":"ok","timestamp":1730491075715,"user_tz":0,"elapsed":280,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}}},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":["Using the Hugging Face pipeline function generator, we pass the few_shot_prompt to the model, setting max_length=150 to control the response length and num_return_sequences=1 to return one response.\n","Few-Shot Influence: With the few-shot examples provided, the model is more likely to respond with a structured explanation resembling the given examples, aligning closely with the original prompt’s intent."],"metadata":{"id":"mInwfkYlTo4w"}},{"cell_type":"code","source":["# Generate the output\n","few_shot_output = generator(few_shot_prompt, max_length=150, num_return_sequences=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6kWfg6_FTkoH","executionInfo":{"status":"ok","timestamp":1730491183978,"user_tz":0,"elapsed":75108,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}},"outputId":"9e6fb5f6-dd8f-406f-d979-a92a03a5136d"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stderr","text":["Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"]}]},{"cell_type":"markdown","source":["We print the output, clearly labeling it as \"Few-shot Output.\" This display allows us to assess whether the model’s response matches the style and detail shown in the examples."],"metadata":{"id":"n84bqz1uTtg0"}},{"cell_type":"code","source":["# Print the output\n","print(\"Few-shot Output:\")\n","print(few_shot_output[0]['generated_text'])\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bobHMJL5TsyG","executionInfo":{"status":"ok","timestamp":1730491224242,"user_tz":0,"elapsed":255,"user":{"displayName":"Mahmoud Mohammed","userId":"00113293506867010646"}},"outputId":"a1656878-817d-4b49-d786-29f07aa87b2c"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Few-shot Output:\n","\n","Explain the concept of blockchain in supply chain management.\n","\n","\n","Example 1:\n","\"Blockchain technology in supply chain management enables secure tracking of goods across each stage. It helps reduce fraud and improves transparency.\"\n","\n","\n","Example 2:\n","\"By using blockchain, companies can ensure that products are sourced ethically and securely. Every transaction is recorded, making auditing straightforward.\"\n","\n","\n","Your turn:\n","\n","Explain how the blockchain technology and smart contracts are expected to change the way the logistics industry operates and what the consequences will be for supply chain managers. Explain the role of supply chain management and the role chain technology will play in supply chain management.\n","\n","Please feel free to email with your own ideas/suggestions/questions at\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"qbOWpaYnUI5A"},"execution_count":null,"outputs":[]}]}